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Constructing K -Connected M -Dominating Sets in Wireless Sensor Networks

Constructing K -Connected M -Dominating Sets in Wireless Sensor Networks. Yiwei Wu, Feng Wang, My T. Thai and Yingshu Li Georgia State University Arizona State University University of Florida. Outline. Introduction Centralized Algorithm – CGA Distributed Algorithm – DDA

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Constructing K -Connected M -Dominating Sets in Wireless Sensor Networks

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  1. Constructing K-Connected M-Dominating Sets in Wireless Sensor Networks Yiwei Wu, Feng Wang, My T. Thaiand Yingshu Li Georgia State University Arizona State University University of Florida

  2. Outline • Introduction • Centralized Algorithm – CGA • Distributed Algorithm – DDA • Simulation Results • Conclusion

  3. Introduction • A Connected Dominating Set (CDS) C of G is a dominating set of G which induces a connected subgraph of G. The nodes in C are called dominators, the others are called dominatees. • A CDS is the earliest proposed candidate to serve as a virtual backbones in WSNs.

  4. Introduction • Using this virtual backbone, a sender can send messages to its neighbouring dominator. Then along the CDS, the messages are sent to the dominator closest to the receiver. Finally, the messages are delivered to the receiver. • k-Connected m-Dominating Set (kmCDS) is necessary for fault tolerance and routing flexibility.

  5. Introduction • k-connected or k-vertex connected • A graph is k-connected if and only if it contains k independent paths between any two vertices. • m-dominating set • If each node not in C is dominated by at least m nodes in C, then C is a m-dominating set. • k-connected m-dominating set

  6. Outline • Introduction • Centralized Algorithm – CGA • Distributed Algorithm – DDA • Simulation Results • Conclusion

  7. Centralized Algorithm CGA • CGA is a greedy algorithm with time complexity of CGA is O(|V |3.5|E|). • The main idea: • construct an m-dominating set C • augment this set C for k-connectivity • remove redundant nodes (optional)

  8. Centralized Algorithm CGA • Notations: • Ni: The number of neighbors of a node i. • ei: The energy of a node i. • Nci: The number of dominator neighbors of node i. • IDi: The ID of node i. • C: A k-connected m-dominating set. • Weight function w(N, e, ID)

  9. Centralized Algorithm CGA

  10. Outline • Introduction • Centralized Algorithm – CGA • Distributed Algorithm – DDA • Simulation Results • Conclusion

  11. Distributed Algorithm - DDA • DDA consists of three phases • Phase 1: Use one of the distributed CDS algorithms to construct a CDSC. • Phase 2: Augment C to a 1-connected m-dominating set by adding m-1 MISs. • Phase 3: Connect set C for k-connectivity.

  12. Distributed Algorithm – DDA phase 3 • How to make C for k-connectivity in a distributed way? • Lemma 1: If G is a k-connected graph, and G’ is obtained from G by adding a new node v with at least k neighbors in G, then G’ is also a k-connected graph.

  13. Distributed Algorithm – DDA phase 3 • Main idea: • The leader builds a k-connected component. • In order to join the k-connected component, one black node adds at most k2 connectors according to the previous lemma.

  14. Distributed Algorithm – DDA phase 3 • Important messages used in DDA: • K-ConnectedComponent (KC) message • RequireConnector (RC) message • ACKConnected (AC) message • ConfirmSuccess (CS) message • ConfirmUnuse (CU) message

  15. Distributed Algorithm – DDA phase 3 • State transition diagram for black nodes

  16. Distributed Algorithm – DDA phase 3 • State transition diagram for white nodes

  17. Distributed Algorithm – DDA RC KC message RC ACK KC message • An example (k=2) 4 5 3 6 2 K-connected component 1

  18. Distributed Algorithm – DDA • An example (k=2) ACK CS 4 5 3 6 2 K-connected component 1

  19. Distributed Algorithm – DDA ACK CU CU • An example (k=2) 4 5 3 6 2 K-connected component 1

  20. Distributed Algorithm – DDA • Lemma 2: Every subset of an MIS is at most three hops away from its complement. • Lemma 3: Let G = (V,E) be any UDG and m be any constant such that δG ≥ m − 1 where δGis the minimum node degree of graph G. Let D∗mbe any optimal m-domination of G and S be any MIS of G. Then |S| ≤ 5m|D∗m |. 1 2 3 4 5

  21. Distributed Algorithm – DDA 1 1 2 2 k k-1 • Theorem: If C is a kmCDS obtained by DDA, then |C| ≤ 5m(k2+1)(m+42)opt, where opt is the size of any optimal kmCDS of the network. • Proof: • Phase 1: 43|S| • Phase 2: (m − 1)|S| • Phase 3: k2 for each black node in phase 1 and 2

  22. Distributed Algorithm – DDA • The message complexity of DDA is O(|V|Δ2) and time complexity is O(mΔ+Diam), where Δ is the maximum node degree and Diam is the diameter of the network.

  23. Outline • Introduction • Centralized Algorithm – CGA • Distributed Algorithm – DDA • Simulation Results • Conclusion

  24. Simulation and Results • 1000m × 1000m area; transmission rage = 250m

  25. Simulation and Results • 1000m × 1000m, transmission rage = 350m

  26. Simulation and Results • Compare CGA with CDSA when k = 2,m = 1

  27. Simulation and Results • Comparision of DDA with k-Coverage (k=2).

  28. Simulation and Results • Comparision of DDA with k-Coverage (k=3).

  29. Outline • Introduction • Centralized Algorithm – CGA • Distributed Algorithm – DDA • Simulation Results • Conclusion

  30. Conclusion • We investigate the problem of constructing a kmCDS for general k and m. • We propose one centralized algorithm CGA and one distributed algorithm DDA. • Our algorithms can obtain good results.

  31. Questions? Thanks!

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